Authors: Yimeng SONG*, The Chinese University of Hong Kong
Topics: Geography and Urban Health, Human-Environment Geography, Environment
Keywords: PM2.5, air pollution, exposure assessment, health impact assessment
Session Type: Paper
Start / End Time: 10:00 AM / 11:40 AM
Room: Endymion, Sheraton, 8th Floor
Presentation File: No File Uploaded
Extremely high fine particulate matter (PM2.5) concentration has been given special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM2.5 exposure assessments have practical limitations, due to the assumption that human beings or air pollution levels are spatially stationary and temporally constant throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM2.5 in Jingjinji area by integrating massive geo-spatial big data and satellite-ground hybrid PM2.5 data. Compared with previous methods, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to accurately assess the actual population exposure to PM2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollution exposures and may hold potential utilities in supporting the healthy alert and related policy-driven environmental actions.